Interactive effects of the low‐carbohydrate diet score and genetic risk score on Hypo‐HDL‐cholesterolemia among Korean adults: A cross‐sectional analysis from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study

Abstract This cross‐sectional study investigated the interaction between the genetic risk score (GRS) and abnormal high‐density lipoprotein (HDL) cholesterol lipid levels, which are modified by low‐carbohydrate diets (LCDs) and their effects on the prevalence of hypo‐HDL‐cholesterolemia (hypo‐HDL‐C) in Korean adults. Baseline data were obtained from the Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES), conducted from 2001 to 2002, that targeted 8,314 Korean adults aged 40–69 years, including old men (47.6%) and women (52.4%), and whole genomic single nucleotide polymorphism (SNP) genotyping was performed. We identified 18 SNPs significantly associated with hypo‐HDL‐C in the proximity of several genes, including LPL, APOA5, LIPC, and CETP, and calculated the GRS. The low‐carbohydrate diet score (LCDS) was calculated on the basis of energy intake information from food frequency questionnaires. Furthermore, we performed multivariable‐adjusted logistic modeling to examine the odds ratio (OR) for hypo‐HDL‐C across tertiles of LCDS and GRS, adjusted for several covariates. Among participants in the highest GRS tertile, those in the highest tertile of the LCDS had a significantly lower risk of hypo‐HDL‐C (OR: 0.759, 95% CI (confidence interval): 0.625–0.923) than those in the lowest tertile of the LCDS. In the joint effect model, the group with the lowest GRS and highest LCDS was found to have the lowest risk of hypo‐HDL‐C prevalence. This study suggests that individuals with a high genetic risk for low HDL concentrations may have a beneficial effect on a lower intake of carbohydrates.

A high dietary carbohydrate-to-fat ratio increases the risk of hypo-HDL-C in women (Lee & An, 2020). In contrast, low-carbohydrate intake decreases the risk of hypo-HDL-C among Korean adults (Kim et al., 2019).
Other major risk factors for hypo-HDL-C are genetic. A study conducted in Roma and Hungarian populations showed that six single nucleotide polymorphisms (SNPs) in lipase C and hepatic type (LIPC) and five SNPs in cholesterol ester transfer protein (CETP) were significantly associated with an increasing TG/HDL cholesterol ratio, which raises the risk of CVD (Piko et al., 2020). SNP rs6564851 in beta-carotene oxygenase 1 (BCMO1) is positively associated with HDL cholesterol levels among the US population (Clifford et al., 2013). However, most previous studies have investigated only single SNPs to reveal the factors associated with HDL cholesterol levels, and more studies on the association of whole genetic effects with HDL cholesterol levels are required.
To investigate the associations between HDL cholesterol and diseases, studies investigating interactions between genetic factors and diet are necessary. A study conducted in Framingham reported that a high polyunsaturated fatty acid (PUFA) intake is more significantly associated with an increased HDL cholesterol concentration in women having the apolipoprotein A5 (APOA1) G-A polymorphism than in those having the G-G polymorphism (Ordovas et al., 2002). Furthermore, the interaction between dietary intake of n-3 and n-6 PUFA and fatty acid desaturase 1 (FADS1) genetic polymorphisms may play a role in modulating plasma cholesterol concentrations (Lu et al., 2010).
Most studies focus on the interaction of a single causative SNP with a dietary factor in hypo-HDL-C. The genetic risk score (GRS) is a useful measurement to evaluate the effect of multiple associated loci of interest (Iwata et al., 2012;Peterson et al., 2011). A study on the Southeast Asian population showed a significant correlation of GRS with waist circumference and TG levels influenced by lowprotein intake (Alsulami et al., 2020).
The interactive effects between dietary and genetic factors on hypo-HDL-C among the Korean population have not been previously investigated using a genome-wide association study (GWAS).
Hence, we investigated the interactive effects of low-carbohydrate diet score (LCDS) and GRS on hypo-HDL-C in Korean adults based on data from the Ansan and Ansung study, which could facilitate personalized nutrition.

| Study population
Baseline data were obtained from the Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES) conducted from 2001 to 2002 among 8314 Korean adults aged 40-69 years including men (47.6%) and women (52.4%), and whole genomic SNP genotyping was performed. This study was performed to investigate the environmental and genetic causes of common chronic diseases, such as metabolic diseases and CVDs, in South Koreans (Kim, Han et al., 2017).
From the 10,030 subjects initially included in the Ansan and Ansung study, we excluded subjects without dietary intake data (n = 697), HDL cholesterol data, and those with data outside the 3SD range from the mean value (n = 111). We excluded participants taking hyperlipidemia medications (n = 56), participants with an inadequate range of energy intake (<500 kcal or >5000 kcal), those with a body mass index (BMI) range over 50 kg/m 2 (n = 380), and those with missing genotype data (n = 472). In addition, all participants with a history of hyperlipidemia were excluded. Thus, 8314 participants were included (Figure 1). The protocol for the use of data was obtained from the KoGES (4851-302) and approved by the National Research Institute of Health, Centers for Disease Control and Prevention, Ministry for Health and Welfare, Republic of Korea.

| Definition of hypo-HDL-C
High-density lipoprotein cholesterol levels were measured in blood samples collected after 8-14 h of overnight fasting. Participants with HDL cholesterol levels below 40 mg/dl were diagnosed with hypo-HDL-C based on the Korean Society of Lipid and Atherosclerosis guidelines (Cohen et al., 2004).

| Low-carbohydrate diet score (LCDS) measurement
Total energy and macronutrient intake data were collected using 106 food items from valid semiquantitative food frequency questionnaires developed for KoGES and were validated in a previous study (Ahn et al., 2007). According to the 2020 Dietary Reference Intakes for Korean (KDRIs) by The Ministry of Health and Welfare Korean Nutrition Society, the recommended intake of carbohydrates for the Korean population is 55%-65% of the total energy intake.
For the LCDS, carbohydrate, protein, and fat intake was calculated using standard conversion factors as a percentage of energy (4 kcal/g for carbohydrate and protein, 9 kcal/g for fat). Participants were given 0 to 10 points for each three nutrient intake levels by sex. The sum of the points represented the LCDS, which ranged from 0 to 30 points. A high or low LCDS indicates a lower-or highercarbohydrate diet, respectively, specific to this study population (Table 1). The LCDSs were divided according to sex and age into tertiles (T1, T2, and T3 for the lowest, middle, and highest LCDS group, respectively) for further analysis.

| GRS estimation
Genomic DNA was extracted from peripheral leukocytes collected from study participants (Cho et al., 2009). Genotyping was performed using the Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix, Inc.). Available SNPs were filtered for call rate, minor allele frequency (MAF), and Hardy-Weinberg equilibrium by referring to the relevant criteria (Cho et al., 2009). SNP imputation was conducted using the IMPUTE program (Marchini et al., 2007).
The imputation was based on NCBI build 36 and dbSNP build 126 and used HapMap data from 90 individuals from Tokyo, Japan, and Han Chinese in Beijing, China, founders in HapMap, as a reference (HapMap release 22). After removing SNPs with MAF < 0.01 and SNP missing rate >0.05, 1.8 million imputed SNPs were collected for association analyses with the hypo-HDL-C trait (Cho et al., 2009).
A GWAS between SNPs and HDL cholesterol levels was performed using PLINK version 1.09 (https://www.cog-genom ics.org/ plink2) tested by the linear model after adjustment for formulation and statistical model description (Purcell et al., 2007). Linear association analysis was performed using 1,590,162 SNPs. The threshold criterion was set to −log 10 p-value >6 for 6K SNPs from GWAS (Kato et al., 2011;Kim, Han et al., 2017). Linkage disequilibrium (LD) clumping and LD analysis were performed using the causal variants identification in associated regions (CAVIAR) program to identify causal SNPs located in trait-associated regions (Hormozdiari et al., 2014). SNP locations and nomenclature were defined by Ensembl. The GRS was calculated for each subject with the 18 most strongly associated SNPs according to the following model (1): where Y ijk is the observed value of the hypo-HDL-C trait, μ is the mean of the samples, β 1 (age i ) is the covariate of age (level: 40-69), β 2 (sex i ) is the covariate of sex, SNP k is the effect of SNP, and e ijk is the random error. The beta values represent the effective size of increasing the HDL cholesterol level in each SNP. Therefore, a high or low GRS is associated with a high risk or low risk of having low HDL cholesterol levels, respectively. For the analysis, GRS was divided into tertiles, T1, T2, and T3, for the lowest, middle, and highest GRS groups, respectively.

| Covariates
The general characteristics of this study population were collected using questionnaires and anthropometric and clinical measurements including height, weight, and BMI (weight [kg] ÷ height 2 [m 2 ]).

| Statistical analysis
Statistical analyses were performed using the SAS software (version 9.4; SAS Institute, Inc.), and a p-value < .05 was considered statistically significant. Categorical data are presented as the number of subjects (%) and continuous data as the means with standard deviation. Multivariable-adjusted logistic regression analysis and joint interaction analysis were performed to examine the OR and 95% CI for hypo-HDL-C across the tertiles of each LCDS and GRS, adjusting for covariates. To analyze the p-values for the trends, regression modeling was performed.

| Characteristics of the study population
General participant characteristics based on LCDS tertiles are described in Table 1. Subjects categorized in LCDS T3 tended to have higher BMI (p = .0098) and house income level (p < .0001), fewer subjects were current drinkers (p < .0001), and more were physically active than those in LCDS T1.
The nutrient intake of subjects across the LCDS tertiles is shown in Table 1 and Table S1 (Supporting Information). Intakes and energy percentage from protein and fat were significantly lower in LCDS T1 than in LCDS T3 (p < .0001). Intakes and energy percentage from protein and fat were significantly higher in LCDS T1 than in LCDS T3 (all p < .0001). Intakes of other nutrients increased as LCDS increased (all p < .0001) (Table S1).

| Hypo-HDL-C GWAS
A set of 1,590,162 SNPs was applied to GWAS ( Table 2). The greatest and lowest numbers of SNPs were located on human chromosome 2 and 19, respectively. The average interval of the available SNPs located by chromosome was 1,856.0 kb; chromosome 19 and 6 had the widest and narrowest interval, respectively.
Genome-wide association study was performed to identify significant loci related to hypo-HDL-C. Among the 86,465 significant SNPs (p < .05), 165 were selected based on −log 10 p-value >6. We identified 18 SNPs after screening for crucial effects on HDL-C levels (Table 3; Figure 2). These SNPs were distributed over seven chromosomes containing 13 candidate genes ( Figure S1). Three SNPs were found on chromosome 8, from 19.9 Mb to 20.0 Mb, close to LPL and SLC18A1, while a single SNP was only significantly annotated on the adenosine triphosphate (ATP)-binding cassette subfamily A member 1 (ABCA1) on chromosome 9. Five SNPs were located in the BUD13 homolog, ZPR1 zinc finger (ZNF259), and APOA5 genes around 116.1 Mb to 116.2 Mb on chromosome 11, respectively. Among them, three SNPs were located on the same LD block ( Figure S2). In the region of 111.3 Mb to 111.9 Mb on chromosome 12, three SNPs were located in the myosin light chain 2 (MYL2), ribosomal protein L6 pseudogene 27 (RPL6P27), and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes. Another three SNPs were located in the aquaporin 9 (AQP9) and LIPC genes from 56.4 kb to 56.5 kb on chromosome 15. Two SNPs were commonly located in CETP on chromosome 16. The last significant SNP was annotated on the apolipoprotein C1 (APOC1) gene on chromosome 19. The rs6999158 SNP showed the most significant association with HDL levels (−log 10 p-value = 18.4) and was in the intergenic region near SLC18A1. The second most significant SNP, rs1011685, was located in LPL. rs16940212, rs2160669, and rs2075291 were located close to LIPC, ZNF259, and APOA5, respectively. The SNP with the highest absolute beta value (−2.6) was rs2075291 and had a significant effect on hypo-HDL-C (−log 10 p-value < 6). Based on the 18 SNPs with the strongest effect on hypo-HDL-C, we calculated GRS for further analysis.

| Interactive effects of LCDS and GRS on the prevalence of hypo-HDL-C
The ORs and 95% CIs of hypo-HDL-C across the interaction of LCDS tertiles and HDL levels are presented in Table 4. The hypo-HDL-C prevalence was analyzed based on changes in LCDS in each GRS level, and the lowest tertile (T1) in each GRS level was used as the reference group. In the GRS T3 group, subjects in the highest LCDS tertile had a significantly decreased hypo-HDL-C prevalence compared with those in LCDS T1 in both models 1 (p for trend = .0054) and 2 (p for trend = .0117). The subjects in the T2 and T3 LCDS groups in GRS T2 showed a borderline decrease, the hypo-HDL-C prevalence was not significant compared with that in LCDS T1 (p for trend = .0767, 0.0631 in models 1 and 2, respectively). The subjects in LCDS T3 in GRS T1 had significantly decreased hypo-HDL-C prevalence in model 1 (p for trend = 0.0264); however, the association was attenuated after adjustment in model 2 (p for trend = 0.1245), and no significant interaction term was observed.
In the joint effect model with low LCDS and low GRS as a reference, the lowest prevalence risk for borderline hypo-HDL-C was observed in individuals with high (T3) LCDS and low (T1) GRS (p for trend < .0001). The highest prevalence risk was observed in individuals with low LCDS and high GRS (p for trend < .0001) (Table 5 and Figure 3). In addition, at any level of GRS, a higher LCDS decreased the hypo-HDL-C prevalence, and at any level of LCDS, a higher GRS increased the hypo-HDL-C prevalence.

| DISCUSS ION
We examined the interactions between LCDS based on the energy percentage of carbohydrate, protein, and fat intake and the GRS using 18 significant SNPs derived from the GWAS for HDL cholesterol. The LCDS not only indicated low-carbohydrate content but also high protein and fat content, which could affect HDL cholesterol positively through the action of fats such as PUFA. A higher LCDS was associated with a decreased prevalence of hypo-HDL-C among subjects with a higher GRS but not in those with lower GRS.
The lowest risk of hypo-HDL-C prevalence was observed in individuals with both low GRS for low HDL cholesterol level and high LCDS.
However, no clear interaction term was observed between GRS and LCDS. These results suggest that a low-carbohydrate diet might attenuate genetic influences on hypo-HDL-C.
The results showed that the risk of hypo-HDL-C significantly decreases as the LCDS increases among Korean adults, which is consistent with the findings in previous studies (Kim et al., 2019;Park et al., 2010). Consistent with the findings in other studies, a high LCDS increased HDL cholesterol levels (Brehm et al., 2003;Nordmann et al., 2006;Yancy et al., 2004), possibly through a mechanism underlying the effects of dietary macronutrient composition.
A high LCDS diet derives its greatest proportion of energy from fat rather than from carbohydrate, which increases the HDL cholesterol level (Bazzano et al., 2014). Furthermore, the intake of lauric acidrich fats increases HDL cholesterol levels (Mensink et al., 2003). Although hypo-HDL-C was significantly decreased by a high LCDS and low GRS, LCDS did not show a significant decrease in the prevalence of hypo-HDL-C in the highest GRS group, indicating that genetic factors also moderate the levels of HDL cholesterol.
Similar results have shown that the highest risk of hypo-HDL-C occurred in subjects with presumably decreased activities of two proteins due to an interaction between LPL and CETP polymorphisms (Corsetti et al., 2011 (Junyent et al., 2009). This suggests that Asians and Americans have different genetic risks.
For GRS-related HDL, the Framingham Heart Study evaluated the GRS for lipid levels using genome-wide markers based on their study. Lipid levels, including HDL cholesterol, were more highly associated with weighted than with unweighted GRS variables (Piccolo et al., 2009). CVD prevalence among participants with familial hypercholesterolemia indicated that the GRS based on related SNPs could modify disease phenotype, facilitating a personalized therapy approach (Paquette et al., 2017).
Large-scale analysis of GWAS has advanced our understanding of the role of genetic variation in complex human diseases, such as diabetes (Barrett et al., 2009;Frayling et al., 2007;Onengut-Gumuscu et al., 2015). GWAS has provided new insights into disease mechanisms and associations with dietary factors (Brunkwall et al., 2016).   F I G U R E 2 Manhattan plot of the results of genome-wide association analysis of high-density lipoprotein HDL cholesterol levels. The x axis shows the chromosomal position, and the y axis shows the −log 10 p-values using the trend test for SNPs distributed across the entire genome. The red line indicates the signals with p < 10 -6 detected in the genome-wide association study (GWAS).

CO N FLI C T O F I NTE R E S T
The authors declare that they have no competing interests.

E TH I C A L A PPROVA L
The protocol of the current study was approved by the Institutional Review Board (IRB) of Chung-Ang University (IRB no. 1041078-201,908-HRBR-239-01).

CO N S E NT TO PA RTI CI PATE
Written informed consent forms were signed by all participants.

CO N S E NT FO R PU B LI C ATI O N
Not applicable.

DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets used and/or analyzed during the current study are